bbf15ad690
The SP7 controller's flatness gate reads per-branch Q-variance from ISV[Q_VAR_PER_BRANCH_BASE=222..226) but that signal was not visible in HEALTH_DIAG. The existing "var_q" in the main HEALTH_DIAG line is realized step-return variance per magnitude bin from gpu_experience_collector — a trade-outcome metric, semantically distinct from per-branch Q-output variance. Added one emit line immediately after cql_budget_per_branch: HEALTH_DIAG[E]: q_var_per_branch [dir=X.XXXX mag=X.XXXX ord=X.XXXX urg=X.XXXX] Reads ISV[222..226) via read_isv_signal_at — the same slots written by q_branch_stats_kernel.cu (scratch slot 2 per branch) and routed into ISV by apply_pearls_ad_kernel in launch_sp5_pearl_1_atom. No new ISV slots, no kernel change, no StateResetRegistry entry. Class 2 signal (mag_concat_scale / q_rms): Option A infeasible — q_rms is a per-sample register variable in mag_concat_qdir with no existing ISV slot; h_s2_rms_ema at ISV[96] is the only available proxy. Option B (new ISV slot) blocked pending explicit controller OK. See audit doc for full stop-and-report rationale. Files touched: crates/ml/src/trainers/dqn/trainer/training_loop.rs (+28 LOC) docs/dqn-wire-up-audit.md (+13 LOC) [ISV slot decision: Option A reused existing Q_VAR_PER_BRANCH_BASE=222..226] Cargo check workspace clean. State-reset contract test passes. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.